def convert_search_space(spec: Dict, prefix: str = "") -> Dict:
spec = copy.deepcopy(spec)
resolved_vars, domain_vars, grid_vars = parse_spec_vars(spec)
if not domain_vars and not grid_vars:
return {}
if grid_vars:
raise ValueError(
"Grid search parameters cannot be automatically converted "
"to a HyperOpt search space.")
def resolve_value(par: str, domain: Domain) -> Any:
quantize = None
sampler = domain.get_sampler()
if isinstance(sampler, Quantized):
quantize = sampler.q
sampler = sampler.sampler
if isinstance(domain, Float):
if isinstance(sampler, LogUniform):
if quantize:
return hpo.hp.qloguniform(par, np.log(domain.lower),
np.log(domain.upper),
quantize)
return hpo.hp.loguniform(par, np.log(domain.lower),
np.log(domain.upper))
elif isinstance(sampler, Uniform):
if quantize:
return hpo.hp.quniform(par, domain.lower, domain.upper,
quantize)
return hpo.hp.uniform(par, domain.lower, domain.upper)
elif isinstance(sampler, Normal):
if quantize:
return hpo.hp.qnormal(par, sampler.mean, sampler.sd,
quantize)
return hpo.hp.normal(par, sampler.mean, sampler.sd)
elif isinstance(domain, Integer):
if isinstance(sampler, LogUniform):
if quantize:
return hpo.base.pyll.scope.int(
hpo.hp.qloguniform(
par,
np.log(domain.lower),
np.log(domain.upper),
quantize,
))
return hpo.base.pyll.scope.int(
hpo.hp.qloguniform(par, np.log(domain.lower),
np.log(domain.upper - 1), 1.0))
elif isinstance(sampler, Uniform):
if quantize:
return hpo.base.pyll.scope.int(
hpo.hp.quniform(par, domain.lower,
domain.upper - 1, quantize))
return hpo.hp.uniformint(par,
domain.lower,
high=domain.upper - 1)
elif isinstance(domain, Categorical):
if isinstance(sampler, Uniform):
return hpo.hp.choice(
par,
[
HyperOptSearch.convert_search_space(category,
prefix=par)
if isinstance(category, dict) else
HyperOptSearch.convert_search_space(
dict(enumerate(category)), prefix=f"{par}/{i}")
if isinstance(category, list) else
resolve_value(f"{par}/{i}", category)
if isinstance(category, Domain) else category
for i, category in enumerate(domain.categories)
],
)
raise ValueError("HyperOpt does not support parameters of type "
"`{}` with samplers of type `{}`".format(
type(domain).__name__,
type(domain.sampler).__name__))
for path, domain in domain_vars:
par = "/".join(
[str(p) for p in ((prefix, ) + path if prefix else path)])
value = resolve_value(par, domain)
assign_value(spec, path, value)
return spec
def convert_search_space(spec: Dict, prefix: str = "") -> Dict:
spec = copy.deepcopy(spec)
resolved_vars, domain_vars, grid_vars = parse_spec_vars(spec)
if not domain_vars and not grid_vars:
return {}
if grid_vars:
raise ValueError(
"Grid search parameters cannot be automatically converted "
"to a HyperOpt search space.")
def resolve_value(par: str, domain: Domain) -> Any:
quantize = None
sampler = domain.get_sampler()
if isinstance(sampler, Quantized):
quantize = sampler.q
sampler = sampler.sampler
if isinstance(domain, Float):
if isinstance(sampler, LogUniform):
if quantize:
return hpo.hp.qloguniform(par, domain.lower,
domain.upper, quantize)
return hpo.hp.loguniform(par, np.log(domain.lower),
np.log(domain.upper))
elif isinstance(sampler, Uniform):
if quantize:
return hpo.hp.quniform(par, domain.lower, domain.upper,
quantize)
return hpo.hp.uniform(par, domain.lower, domain.upper)
elif isinstance(sampler, Normal):
if quantize:
return hpo.hp.qnormal(par, sampler.mean, sampler.sd,
quantize)
return hpo.hp.normal(par, sampler.mean, sampler.sd)
elif isinstance(domain, Integer):
if isinstance(sampler, Uniform):
if quantize:
logger.warning(
"HyperOpt does not support quantization for "
"integer values. Reverting back to 'randint'.")
if domain.lower != 0:
raise ValueError(
"HyperOpt only allows integer sampling with "
f"lower bound 0. Got: {domain.lower}.")
if domain.upper < 1:
raise ValueError(
"HyperOpt does not support integer sampling "
"of values lower than 0. Set your maximum range "
"to something above 0 (currently {})".format(
domain.upper))
return hpo.hp.randint(par, domain.upper)
elif isinstance(domain, Categorical):
if isinstance(sampler, Uniform):
return hpo.hp.choice(par, [
HyperOptSearch.convert_search_space(category,
prefix=par)
if isinstance(category, dict) else
HyperOptSearch.convert_search_space(
dict(enumerate(category)), prefix=f"{par}/{i}")
if isinstance(category, list) else
resolve_value(f"{par}/{i}", category) if isinstance(
category, Domain) else category
for i, category in enumerate(domain.categories)
])
raise ValueError("HyperOpt does not support parameters of type "
"`{}` with samplers of type `{}`".format(
type(domain).__name__,
type(domain.sampler).__name__))
for path, domain in domain_vars:
par = "/".join(
[str(p) for p in ((prefix, ) + path if prefix else path)])
value = resolve_value(par, domain)
assign_value(spec, path, value)
return spec
